Dealing with AI backlash

Trust can be hard to repair when AI goes wrong

By Christopher Null

Avoiding a backlash requires a sustained record of
success and openness about problems and how you plan to address
them

A majority of workers want their companies to be more
transparent about how they plan to use AI

It’s been a rough few months for artificial intelligence. In March
2018, a pedestrian was killed by a
self‑driving vehicle operated by Uber. A few days later, a Tesla, traveling in
autonomous Autopilot mode, slammed into a concrete barrier, killing
its driver.

A few weeks after that came a far more benign AI mishap, but one
that left just as many people feeling unsettled. Google Assistant,
Google’s voice‑enabled AI platform, showed off its new conversational
features by phoning a restaurant to make a dinner reservation.

The assistant carried on a three‑minute conversation with the host
and successfully booked a table. But it never identified itself as an
AI assistant to the host, who believed he was talking to a person—a
revelation that media outlets called creepy, crazy, and “scary as hell.”

It’s not just consumers feeling the impact of runaway AIs.
Microsoft’s experimental Tay chatbot was designed to learn how to
interact with customers via Twitter. The company was horrified to see
Tay evolve into a misogynistic racist in less than 24 hours, leaving
it publicly embarrassed.

Thousands of companies are offering new AI products and
incorporating the technology into applications. But as these and other
incidents show, promising but unpredictable technology can become an
overnight pariah, leaving companies scrambling to deal with the backlash.

In the wake of the Uber and Tesla accidents, some manufacturers
began pulling back on
self‑driving programs. Several lawmakers quickly moved to ban them outright.
In response to the outcry over the public demo of the restaurant call,
Google officials announced that future tests would start with a clear
disclosure: “I’m the Google Assistant and I’m calling for a client.”
This move failed to quell public concern.

People have long worried about
the threat of intelligent machines slipping out of our control. That
presents an ongoing challenge for companies experimenting with AI
technology. How do you build support for AI initiatives and products
that are vulnerable to negative public sentiment? And how do you
rebound from failures that are sure to come?

Successful technologies build and maintain trust, says Keng Siau, a
professor at the Missouri University of Science and Technology. “AI is
supposed to make our lives better,” Siau says, but that’s far from a
predictable outcome. “Smarter companies are being proactive about this
by developing trust.”

Siau breaks down trust into two general types: initial trust and
continuous trust. Creating initial trust is the easier challenge. A
successful trial run of a new product or a simple friendly face can
help to establish initial trust. That’s one reason why robots
fashioned after humans are so popular; it’s easier for people to
“establish an emotional connection” with them,” says Siau.

Longer term, companies must prove a product or service’s reliability
and usability, showing that it’s not prone to downtime or accidents,
that it can collaborate effectively with humans, and that it exhibits
strong security.

In the case of driverless cars, trust remains a long‑term
challenge—and can be destroyed overnight. In January 2017, 78% of U.S.
drivers said they would be afraid to ride in a fully autonomous car,
according to an American Automobile
Association survey. By December 2017, that number fell to
63%. Then came the Tesla and Uber incidents, which dented public
confidence in autonomous vehicles. In the most recent survey update,
from April 2018, 73% of drivers still wouldn’t trust a driverless car.

Rebuilding Bonds

When something bad happens, a backlash is inevitable—from customers,
employees, the public, or all three. “Fixing the problem takes a long
time and a lot of energy,” says Siau. “You have to assure the public
that you will investigate, report on the issues you identified, and so on.”

Such a report might include noting how many hours of testing were
undertaken, whether the issue was an isolated incident, and what’s
being done to fix it. Tesla, for example, has updated the public
twice about the accident, outlining in significant detail
what went wrong (the car had been in a prior accident and a key part
had not been repaired), and citing its overall safety statistics while
noting the impossibility of achieving a perfect safety record.

When problems occur, it’s also critical for companies to help people
understand the underlying technologies. People naturally fear the
unknown. “You need to demystify the process by which the product
works,” Siau says.

For example, it isn’t good enough for an AI application to make a
medical recommendation to a patient. Trust is only built when the
patient understands why it made the recommendation. “The key is
to be even more transparent,” says Siau.

The same principle applies internally and building trust with
employees, who may have lingering concerns not just about AI products
but future competition for their jobs. “Companies should be candid
about their automation plans and communicate with employees about
retraining, redeployment, and continuous education possibilities,”
says Siau.

The most recent workplace statistics suggest employers have their
work cut out for them. Nearly 60% of organizations today have yet to
discuss the potential impact of AI on their workforce with employees,
according to a 2018 study by the
Workforce Institute. And 61% of employees say they would like to see
their companies be more transparent about future plans for AI.

No matter how a company’s plans for AI may evolve, the backlash
potential suggests that when it comes to your workforce, any
successful AI strategy will require a large dose of humanity.

Christopher Null is a longtime technology and business
journalist who contributes regularly to TechHive,
PCWorld, Wired, and other publications.